Class 10 Mini Project
Yuhan Zhang (PID: A13829264)
10/29/2021
1. Importing candy data
candy_file <- "candy-data.csv"
candy = read.csv(candy_file, row.names=1)
rownames(candy) <- gsub("Õ", "'", rownames(candy))
head(candy)## chocolate fruity caramel peanutyalmondy nougat crispedricewafer
## 100 Grand 1 0 1 0 0 1
## 3 Musketeers 1 0 0 0 1 0
## One dime 0 0 0 0 0 0
## One quarter 0 0 0 0 0 0
## Air Heads 0 1 0 0 0 0
## Almond Joy 1 0 0 1 0 0
## hard bar pluribus sugarpercent pricepercent winpercent
## 100 Grand 0 1 0 0.732 0.860 66.97173
## 3 Musketeers 0 1 0 0.604 0.511 67.60294
## One dime 0 0 0 0.011 0.116 32.26109
## One quarter 0 0 0 0.011 0.511 46.11650
## Air Heads 0 0 0 0.906 0.511 52.34146
## Almond Joy 0 1 0 0.465 0.767 50.34755What is in the dataset?
Q1. How many different candy types are in this dataset?
candy.numType <- nrow(candy)
candy.numType## [1] 85Q2. How many fruity candy types are in the dataset?
fruityNum <- sum(candy$fruity)
fruityNum## [1] 382. What is your favorate candy?
One of the most interesting variables in the dataset is winpercent. For a given candy this value is the percentage of people who prefer this candy over another randomly chosen candy from the dataset (what 538 term a matchup). Higher values indicate a more popular candy.
We can find the winpercent value for Twix by using its name to access the corresponding row of the dataset. This is because the dataset has each candy name as rownames (recall that we set this when we imported the original CSV file). For example the code for Twix is:
candy["Twix", ]$winpercent## [1] 81.64291Q3. What is your favorite candy in the dataset and what is it’s
winpercentvalue?
candy["WelchÕs Fruit Snacks", ]$winpercent## [1] NAQ4. What is the
winpercentvalue for “Kit Kat”?
candy["Kit Kat", ]$winpercent## [1] 76.7686Q5. What is the
winpercentvalue for “Tootsie Roll Snack Bars”?
candy["Tootsie Roll Snack Bars", ]$winpercent## [1] 49.6535Side-note: the skimr::skim() function
There is a useful skim() function in the skimr package that can help give you a quick overview of a given dataset. Let’s install this package and try it on our candy data.
# install.packages("skimr")
library("skimr")
skim(candy)| Name | candy |
| Number of rows | 85 |
| Number of columns | 12 |
| _______________________ | |
| Column type frequency: | |
| numeric | 12 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| chocolate | 0 | 1 | 0.44 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| fruity | 0 | 1 | 0.45 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| caramel | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| peanutyalmondy | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| nougat | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| crispedricewafer | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| hard | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| bar | 0 | 1 | 0.25 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| pluribus | 0 | 1 | 0.52 | 0.50 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| sugarpercent | 0 | 1 | 0.48 | 0.28 | 0.01 | 0.22 | 0.47 | 0.73 | 0.99 | ▇▇▇▇▆ |
| pricepercent | 0 | 1 | 0.47 | 0.29 | 0.01 | 0.26 | 0.47 | 0.65 | 0.98 | ▇▇▇▇▆ |
| winpercent | 0 | 1 | 50.32 | 14.71 | 22.45 | 39.14 | 47.83 | 59.86 | 84.18 | ▃▇▆▅▂ |
Use skim() and answer the following:
Q6. Is there any variable/column that looks to be on a different scale to the majority of the other columns in the dataset?
Winpercent is in a different scale to majority since it has standard deviation of 14.7.
Q7. What do you think a zero and one represent for the candy$chocolate column?
0: no chocolate 1: contain chocolate
A good place to start any exploratory analysis is with a histogram. You can do this most easily with the base R function hist(). Alternatively, you can use ggplot() with geom_hist(). Either works well in this case and (as always) its your choice.
Q8. Plot a histogram of winpercent values
library(ggplot2)
win.hist <- ggplot(candy, aes(x = winpercent)) +
geom_histogram()
win.hist## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Q9. Is the distribution of winpercent values symmetrical?
Yes the distribution is symmetrical
Q10. Is the center of the distribution above or below 50%?
median(candy$winpercent)## [1] 47.82975The center is below 50%.
Q11. On average is chocolate candy higher or lower ranked than fruit candy?
candy.chocolateType <- candy$winpercent[as.logical(candy$chocolate)]
candy.fruityType <- candy$winpercent[as.logical(candy$fruity)]
t.test(candy.chocolateType, candy.fruityType)##
## Welch Two Sample t-test
##
## data: candy.chocolateType and candy.fruityType
## t = 6.2582, df = 68.882, p-value = 2.871e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 11.44563 22.15795
## sample estimates:
## mean of x mean of y
## 60.92153 44.11974On average chocolate is higher ranked than fruity type.
Q12. Is this difference statistically significant?
There is significant difference (p = 2.871e-08).
3. Overall Candy Rankings
Let’s use the base R order() function together with head() to sort the whole dataset by winpercent. Or if you have been getting into the tidyverse and the dplyr package you can use the arrange() function together with head() to do the same thing and answer the following questions:
Q13. What are the five least liked candy types in this set?
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unioncandy.leastFav <- candy %>% arrange(winpercent) %>% head(5)
candy.leastFav## chocolate fruity caramel peanutyalmondy nougat
## Nik L Nip 0 1 0 0 0
## Boston Baked Beans 0 0 0 1 0
## Chiclets 0 1 0 0 0
## Super Bubble 0 1 0 0 0
## Jawbusters 0 1 0 0 0
## crispedricewafer hard bar pluribus sugarpercent pricepercent
## Nik L Nip 0 0 0 1 0.197 0.976
## Boston Baked Beans 0 0 0 1 0.313 0.511
## Chiclets 0 0 0 1 0.046 0.325
## Super Bubble 0 0 0 0 0.162 0.116
## Jawbusters 0 1 0 1 0.093 0.511
## winpercent
## Nik L Nip 22.44534
## Boston Baked Beans 23.41782
## Chiclets 24.52499
## Super Bubble 27.30386
## Jawbusters 28.12744Q14. What are the top 5 all time favorite candy types out of this set?
candy.mostFav <- candy %>% arrange(desc(winpercent)) %>% head(5)
candy.mostFav## chocolate fruity caramel peanutyalmondy nougat
## Reese's Peanut Butter cup 1 0 0 1 0
## Reese's Miniatures 1 0 0 1 0
## Twix 1 0 1 0 0
## Kit Kat 1 0 0 0 0
## Snickers 1 0 1 1 1
## crispedricewafer hard bar pluribus sugarpercent
## Reese's Peanut Butter cup 0 0 0 0 0.720
## Reese's Miniatures 0 0 0 0 0.034
## Twix 1 0 1 0 0.546
## Kit Kat 1 0 1 0 0.313
## Snickers 0 0 1 0 0.546
## pricepercent winpercent
## Reese's Peanut Butter cup 0.651 84.18029
## Reese's Miniatures 0.279 81.86626
## Twix 0.906 81.64291
## Kit Kat 0.511 76.76860
## Snickers 0.651 76.67378To examine more of the dataset in this vain we can make a barplot to visualize the overall rankings. We will use an iterative approach to building a useful visulization by getting a rough starting plot and then refining and adding useful details in a stepwise process.
ggplot(candy, aes(winpercent, rownames(candy))) +
geom_col()
Q16. This is quite ugly, use the reorder() function to get the bars sorted by winpercent?
ggplot(candy, aes(winpercent, reorder(rownames(candy), winpercent))) +
geom_col()
Time to add some useful color
Let’s setup a color vector (that signifies candy type) that we can then use for some future plots. We start by making a vector of all black values (one for each candy). Then we overwrite chocolate (for chocolate candy), brown (for candy bars) and red (for fruity candy) values.
my_cols=rep("black", nrow(candy))
my_cols[as.logical(candy$chocolate)] = "chocolate"
my_cols[as.logical(candy$bar)] = "brown"
my_cols[as.logical(candy$fruity)] = "pink"Now let’s try our barplot with these colors. Note that we use fill=my_cols for geom_col(). Experement to see what happens if you use col=mycols.
ggplot(candy) +
aes(winpercent, reorder(rownames(candy),winpercent)) +
geom_col(fill=my_cols) 
Now, for the first time, using this plot we can answer questions like:
Q17. What is the worst ranked chocolate candy?
sixlets
Q18. What is the best ranked fruity candy?
starbusts
4. Taking a look at pricepercent
What about value for money? What is the the best candy for the least money? One way to get at this would be to make a plot of winpercent vs the pricepercent variable. The pricepercent variable records the percentile rank of the candy’s price against all the other candies in the dataset. Lower vales are less expensive and high values more expensive.
To this plot we will add text labels so we can more easily identify a given candy. There is a regular geom_label() that comes with ggplot2. However, as there are quite a few candys in our dataset lots of these labels will be overlapping and hard to read. To help with this we can use the geom_text_repel() function from the ggrepel package.
# install.packages("ggrepel")
library(ggrepel)
# How about a plot of price vs win
my_cols[as.logical(candy$fruity)] <- "red"
ggplot(candy) +
aes(winpercent, pricepercent, label=rownames(candy)) +
geom_point(col=my_cols) +
geom_text_repel(col=my_cols, size=3.3)## Warning: ggrepel: 10 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Q19. Which candy type is the highest ranked in terms of winpercent for the least money - i.e. offers the most bang for your buck?
Chocolate (Reese’s miatures at the right bottom corner)
Q20. What are the top 5 most expensive candy types in the dataset and of these which is the least popular?
ord <- order(candy$pricepercent, decreasing = TRUE)
head( candy[ord,c(11,12)], n=5 )## pricepercent winpercent
## Nik L Nip 0.976 22.44534
## Nestle Smarties 0.976 37.88719
## Ring pop 0.965 35.29076
## Hershey's Krackel 0.918 62.28448
## Hershey's Milk Chocolate 0.918 56.49050Nik L Nip
opitional Q21. Make a barplot again with
geom_col()this time using pricepercent and then improve this step by step, first ordering the x-axis by value and finally making a so called “dot chat” or “lollipop” chart by swappinggeom_col()forgeom_point()+geom_segment().
# Make a lollipop chart of pricepercent
ggplot(candy) +
aes(pricepercent, reorder(rownames(candy), pricepercent)) +
geom_segment(aes(yend = reorder(rownames(candy), pricepercent),
xend = 0), col="gray40") +
geom_point()
5. Exploring the correlation structure
Now that we’ve explored the dataset a little, we’ll see how the variables interact with one another. We’ll use correlation and view the results with the corrplot package to plot a correlation matrix.
# install.packages("corrplot")
library(corrplot)## corrplot 0.90 loadedcij <- cor(candy)
corrplot(cij)
Q22. Examining this plot what two variables are anti-correlated (i.e. have minus values)?
chocolate and fruity
Q23. Similarly, what two variables are most positively correlated?
bar and chocolate
6. Principal Component Analysis
Let’s apply PCA using the prcomp() function to our candy dataset remembering to set the scale=TRUE argument.
pca <- prcomp(candy, scale = TRUE)
summary(pca)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 2.0788 1.1378 1.1092 1.07533 0.9518 0.81923 0.81530
## Proportion of Variance 0.3601 0.1079 0.1025 0.09636 0.0755 0.05593 0.05539
## Cumulative Proportion 0.3601 0.4680 0.5705 0.66688 0.7424 0.79830 0.85369
## PC8 PC9 PC10 PC11 PC12
## Standard deviation 0.74530 0.67824 0.62349 0.43974 0.39760
## Proportion of Variance 0.04629 0.03833 0.03239 0.01611 0.01317
## Cumulative Proportion 0.89998 0.93832 0.97071 0.98683 1.00000Now we can plot our main PCA score plot of PC1 vs PC2.
plot(pca$x[,1:2])
We can change the plotting character and add some color:
plot(pca$x[,1:2], col=my_cols, pch=16)
Using ggplot2:
# Make a new data-frame with our PCA results and candy data
my_data <- cbind(candy, pca$x[,1:3])
p <- ggplot(my_data) +
aes(x=PC1, y=PC2,
size=winpercent/100,
text=rownames(my_data),
label=rownames(my_data)) +
geom_point(col=my_cols)
p
Again we can use the ggrepel package and the function ggrepel::geom_text_repel() to label up the plot with non overlapping candy names like. We will also add a title and subtitle like so:
p + geom_text_repel(size=3.3, col=my_cols, max.overlaps = 7) +
theme(legend.position = "none") +
labs(title="Halloween Candy PCA Space",
subtitle="Colored by type: chocolate bar (dark brown), chocolate other (light brown), fruity (red), other (black)",
caption="Data from 538")## Warning: ggrepel: 35 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
more candy labels you can change the max.overlaps value to allow more overlapping labels or pass the ggplot object p to plotly like so to generate an interactive plot that you can mouse over to see labels:
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutggplotly(p)Let’s finish by taking a quick look at PCA our loadings. Do these make sense to you? Notice the opposite effects of chocolate and fruity and the similar effects of chocolate and bar (i.e. we already know they are correlated).
par(mar=c(8,4,2,2))
barplot(pca$rotation[,1], las=2, ylab="PC1 Contribution")
Q24. What original variables are picked up strongly by PC1 in the positive direction? Do these make sense to you?
fruity, hard, pluribus, yes because fruity candy are usually hard candy or come with pluribus, and fruity candy usually are not chocolate / caramel / nuty / crisp